In 2023, the Italian authorities covering cyber crime have estimated that 137 million euros were embezzled from victims of online fraud. This represents an increase of approximately 20 percent compared to 2022, when victims of online fraud cases in Italy lost an estimated sum of 114.5 million euros to cyber criminals and fraudsters.

Introduction

Identity Theft Statistics: Identity theft occupies a very critical part of the global scene, as stated in 2024, and it does not spare the individual, business, or government. Identity theft is the unauthorized use of other people’s personal information, such as Social Security numbers, credit cards, and online login details, ultimately resulting in financial loss, privacy violations, and mental anguish.

Rapid growth in the area of online transactions and the use of digital platforms shows that incidents of identity theft have increased. This article shows the records generated in contemporary identity theft statistics practice in terms of scale, trends, and impacts in 2025.

In 2022, the most commonly targeted age group by fraudsters was people ages 30 to 39, among whom ******* cases of fraud were reported to the Federal Trade Commission (FTC) in the United States. People aged 60 to 69 were the second most commonly targeted group, with ******* reports of fraud in the same year.

Introduction

Employee Theft Statistics: Organizational behavior is sometimes influenced by employee theft statistics, which is why it remains a big issue for many enterprises globally, translating into huge money losses and diminished organizational morale. For organizations to be able to have perfect preventive measures, they need to understand the scale and scope of this issue fully. In recent years, employee theft has always been a big concern for various companies, regardless of their size.

Reports show that in the USA alone, 30% of all business losses are caused by employee theft, translating to about USD 50 billion worth of annual losses. This makes it a critical issue for companies, regardless of how big they are. This figure represented a slight increase from 2022, when it was about 48 billion US dollars. Retail businesses were more affected, with almost 35% of all theft cases committed by employees.

The recent increases in internal theft can be linked to different issues, such as the increasing sophistication of fraudulent schemes compared to previous years and economic pressures. This article discusses employee theft statistics for 2023 and 2024 as viewed from the perspective of a market researcher.

Introduction

Money Laundering Statistics: Money laundering is the process of concealing the origins of illegally obtained money. These are from illegal activities, such as drug cases, human trafficking, sex work, terrorism, corruption, etc, to generate money. Laundering activities present several challenges for financial institutions, governments, and law enforcement agencies.

The global nature of the economy, the increasing sophistication of financial transactions, and the involvement of anti-money laundering (AML) initiatives all contribute to making the fight against money laundering a complex and ongoing issue, with severe consequences for the integrity of financial systems and economies.

This article includes several statistical analysis from different current sources that explores the methods, implications, and legal frameworks designed to combat money laundering on a global scale.

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(Source: unodc.org)

The Supplemental Fraud Survey (SFS) obtained additional information about fraud-related victimizations so that policymakers; academic researchers; practitioners at the federal, state, and local levels; and special interest groups who are concerned with these crimes can make informed decisions concerning policies and programs. The SFS asked questions related to victims' experiences with fraud. These responses are linked to the National Crime Victimization Survey (NCVS) survey instrument responses for a more complete understanding of the individual victim's circumstances. The 2017 Supplemental Fraud Survey (SFS) was the first implementation of this supplement to the annual NCVS to obtain specific information about fraud-related victimization and disorder on a national level. Since the SFS is a supplement to the NCVS, it is conducted under the authority of Title 34, United States Code, section 10132. Only Census employees sworn to preserve confidentiality may see the completed questionnaires.

In 2022, the District of Columbia was the state with the highest rate of consumer fraud and other related problems, with a rate of 1,747 reports per 100,000 of the population. North Dakota had the lowest rate of consumer fraud reports in that year, at 536 reports per 100,000 of the population.

This category shows all crimes recorded by the police (with the exception of fraud which is recorded centrally as part of Action Fraud).

This data is based on a rolling calendar quarter covering 12 months. Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

In 2022, the most targeted age group for identity theft were 30 to 39 year olds, among whom 286,890 cases were reported to the Federal Trade Commission (FTC) in the United States. The second most targeted age group were those aged 40 to 49, with 212,729 cases of identity theft reported.

This dataset provides a detailed look into transactional behavior and financial activity patterns, ideal for exploring fraud detection and anomaly identification. It contains 2,512 samples of transaction data, covering various transaction attributes, customer demographics, and usage patterns. Each entry offers comprehensive insights into transaction behavior, enabling analysis for financial security and fraud detection applications.

Key Features:

• TransactionID: Unique alphanumeric identifier for each transaction.
• AccountID: Unique identifier for each account, with multiple transactions per account.
• TransactionAmount: Monetary value of each transaction, ranging from small everyday expenses to larger purchases.
• TransactionDate: Timestamp of each transaction, capturing date and time.
• TransactionType: Categorical field indicating 'Credit' or 'Debit' transactions.
• Location: Geographic location of the transaction, represented by U.S. city names.
• DeviceID: Alphanumeric identifier for devices used to perform the transaction.
• IP Address: IPv4 address associated with the transaction, with occasional changes for some accounts.
• MerchantID: Unique identifier for merchants, showing preferred and outlier merchants for each account.
• AccountBalance: Balance in the account post-transaction, with logical correlations based on transaction type and amount.
• PreviousTransactionDate: Timestamp of the last transaction for the account, aiding in calculating transaction frequency.
• Channel: Channel through which the transaction was performed (e.g., Online, ATM, Branch).
• CustomerAge: Age of the account holder, with logical groupings based on occupation.
• CustomerOccupation: Occupation of the account holder (e.g., Doctor, Engineer, Student, Retired), reflecting income patterns.
• TransactionDuration: Duration of the transaction in seconds, varying by transaction type.
• LoginAttempts: Number of login attempts before the transaction, with higher values indicating potential anomalies.

This dataset is ideal for data scientists, financial analysts, and researchers looking to analyze transactional patterns, detect fraud, and build predictive models for financial security applications. The dataset was designed for machine learning and pattern analysis tasks and is not intended as a primary data source for academic publications.

This category shows police-recorded crimes where the offender has stolen property that was in the physical possession of the victim and there was some degree of force towards the property but not the victim (e.g., grabbing a handbag).

This data is based on a rolling calendar quarter covering 12 months. Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

The number of new prison inmates for fraudulent crimes is broken down by age and gender.

The fraud victimization survey was administered by telephone to 400 respondents 18 years or older. Screener items were used to determine whether respondents had been fraud victims. Respondents with victimizations to report were administered the incident report items for up to five fraud incidents. The collection contains two general groups of variables: those pertaining to the individual respondent (Part 1), and those pertaining to the fraud incident (Part 2). Personal information includes basic demographic information (age, race, sex, income) and information about experiences as a victim of crimes other than fraud (robbery, assault, burglary, vehicle theft). Specific questions about fraud victimization experiences distinguished among twenty different types of fraud, including sales of misrepresented products or services, nondelivery of promised work or services, various types of confidence schemes, and fraud relating to credit cards, charities, health products, insurance, investments, or prizes. For each type of fraud the respondent had experienced, a series of questions was asked covering the time, place, and circumstances of the incident, the relationship of the respondent to the person attempting to defraud, the response of the respondent and of other agencies and organizations to the incident, and the financial, psychological, and physical consequences of the victimization experience.

Annual data on the nature of fraud and computer misuse offences. Data for the year ending March 2021 and March 2022 are from the Telephone-operated Crime Survey for England and Wales (TCSEW).

Fraud and fight against fraud

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

The primary purpose of the Identity Theft Supplement (ITS) is to measure the prevalence of identity theft among persons, the characteristics of identity theft victims, and patterns of reporting to the police, credit bureaus, and other authorities. The ITS was also designed to collect important characteristics of identity theft such as how the victim's personal information was obtained; the physical, emotional and financial impact on victims; offender information; and the measures people take to avoid or minimize their risk of becoming an identity theft victim. The information is intended for use by policymakers, academic researchers, practitioners at the Federal, state and local levels, and special interest groups who are concerned with identity theft to make informed decisions concerning policies and programs. Responses are linked to the NCVS survey instrument responses for a more complete understanding of the individual's circumstances. The 2021 Identity Theft Supplement (ITS) was the sixth implementation of this supplement to the annual NCVS to obtain specific information about identity theft-related victimization on a national level. Since the ITS is a supplement to the NCVS, it is conducted under the authority of title 34, United States Code, section 10132. Only Census employees sworn to preserve confidentiality may see the completed questionnaires.

Prisoner's new entry robbery offender number by age group

Public tolerance for corruption within a society significantly influences the prevalence of corrupt practices, but less is known about how this tolerance evolves with social norms. This paper presents experimental evidences demonstrating that the descriptive social norm indicating widespread corruption can lead to increased tolerance for corruptive acts. We introduce an asymmetric information ultimatum game to simulate the interactions between embezzlers and citizens. Game theoretical analysis reveals that victims anticipating corruption will exhibit greater compliance with embezzlement when the offers are evaluated based on descriptive norms. To test the hypothesis, we employ a framing effect to induce variations in descriptive norms within a behavioral experiment. Although the treatment effect is significant only in the subgroup of student cadres, this subgroup demonstrated increased beliefs about embezzlement, greater tolerance for corruption, and a heightened propensity to embezzle when exposed to framings with hierarchical implications. This paper contributes to the corruption literature by examining the effects of descriptive norms on victims’ responses to embezzlement. It offers a more comprehensive perspective on how social standards shape public opinions and corrupt actions, enhancing our understanding of the self-reinforcing nature of corruption.

The number of defendants in custody for fraud crimes by age and gender.